Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for the ongoing support in reviewing manuscripts for Pediatric Neurosurgery:Gregory W. Albert, Little Rock, AR, USARichard C.E. Anderson, Ridgewood, NY, USASuchanda Bhattacharjee, Hyderabad, IndiaDavid Bonda, Seattle, WA, USAFrederick Boop, Memphis, TN, USABruno Braga, Dallas, TX, USAUttara Chatterjee, Kolkata, IndiaIan Coulter, Newcastle upon Tyne, UKAnnie I. Drapeau, Winnipeg, MB, CanadaHassan I. El Shafei, Cairo, EgyptMaurizio Elia, Troina, ItalyRamin Eskandari, Charleston, SC, USAJustin Fraser, Lexington, KY, USAPasquale Gallo, Birmingham, UKCatherine Garcia, Los Angeles, CA, USAFlavio Giordano, Florence, ItalyLance S. Governale, Gainesville, FL, USABenjamin Hall, Liverpool, UKAndrew M. Hersh, Baltimore, MD, USAJames Johnston, Birmingham, AL, USAGokmen Kahilogullari, Ankara, TurkeyJohn Kestle, Salt Lake City, UT, USAJeffrey Leonard, Columbus, OH, USAFrancesco Mangano, Cincinnati, OH, USAJosé Hinojosa Mena-Bernal, Barcelona, SpainPeter Morgenstern, New York, NY, USAOliver Mueller, Dortmund, GermanyDattatraya Muzumdar Parel, Mumbai, IndiaFarideh Nejat, Tehran, IranMark Proctor, Boston, MA, USAJarod L. Roland, St. Louis, MO, USAKazuaki Shimoji, Chiba, JapanMarc Sindou, Lyon, FranceJehuda Soleman, Basel, SwitzerlandPeter Spazzapan, Ljubljana, SloveniaDominic Thompson, London, UKTadanori Tomita, Chicago, IL, USAAlbert Tu, Ottawa, ON, CanadaMichael Vassilyadi, Ottawa, ON, Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.247 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.244 | 0.179 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".